Predictive model for COVID 19 curve - An evolutionary approach (original) (raw)
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PLOS ONE, 2022
Background The COVID-19 epidemic has shown that efficient prediction models are required, and the well-known SI, SIR, and SEIR models are not always capable of capturing the real dynamics. Modified models with novel structures could help identify unknown mechanisms of COVID-19 spread. Objective Our objective is to provide additional insights into the COVID-19 spread mechanisms based on different models' parameterization which was performed using evolutionary algorithms and the first-wave data. Methods Data from the Our World in Data COVID-19 database was analysed, and several models-SI, SIR, SEIR, SEIUR, and Bass diffusion-and their variations were considered for the first wave of the COVID-19 pandemic. The models' parameters were tuned with differential evolution optimization method L-SHADE to find the best fit. The algorithm for the automatic identification of the first wave was developed, and the differential evolution was applied to model parameterization. The reproduction rates (R0) for the first wave were calculated for 61 countries based on the best fits.
Estimation of COVID-19 epidemic curves using genetic programming algorithm
Health Informatics Journal, 2021
This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set.
Applied Mathematics, 2022
Since the onset of the COVID-19 epidemic, the world has been impressed by two things: The number of people infected and the number of deaths. Here, we propose a mathematical model of the spread of this disease, analyze this model mathematically and determine one or more dominant factors in the propagation of the COVID-19 epidemic. We consider the S-E-I-R epidemic model in the form of ordinary differential equations, in a population structured in susceptibles S, exposed E as caregivers, travelers and assistants at public events, infected I and recovered R classes. Here we decompose the recovered class into two classes: The deaths class D and the class of those who are truly healed H. After the model construction, we have calculated the basic reproduction number 0 R , which is a function of certain number of parameters like the size of the exposed class E. In our paper, the mathematical analysis, which consists in searching the equilibrium points and studying their stability, is done. The work identifies some parameters on which one can act to control the spread of the disease. The numerical simulations are done and they illustrate our theoretical analysis.
A SIMPLE AND PREDICTIVE MODEL FOR COVID-19 EVOLUTION IN LARGE SCALE INFECTED COUNTRIES
Journal of Theoretical and Applied Information Technology, 2020
This paper analyzes the reported COVID-19 cases in some largely affected countries around the world and accurately predicts the future values of new, death, recovery, and active COVID-19 cases for effective decision making. The objective is to provide scientific insights for decision makers in these countries to avoid higher levels of severity and large waves of infections. The data for this study were obtained from COVID-19 stylized facts, extracted from the well-known worlddometer website and verified against the WHO's COVID-19 Dashboard, Johns Hopkins University's COVID-19 Dashboard, and CDC from mid of February 2020-Early April 2020. The data covered the highest five affected countries, namely, Brazil, India, Russia, South Africa, and the USA. The data were analyzed using time series forecasting model and presented pictorially in graphs bar charts and pie charts. Based on the outcome of the analyzed data, it was concluded that the predicted COVID-19 cases will reach the peak at the end of September 2020 and if the outbreak is not controlled, the studied countries may face inflated numbers and severe shortage of medical facilities that may worsen the outbreak. The paper concludes by few important recommendations about comprehensive and necessary actions that the government and other policymakers of these countries should take in order to control spread of the virus.
A Novel Heuristic Algorithm for the Modeling and Risk Assessment of the COVID-19 Pandemic Phenomenon
Computer Modeling in Engineering & Sciences
The modeling and risk assessment of a pandemic phenomenon such as COVID-19 is an important and complicated issue in epidemiology, and such an attempt is of great interest for public health decision-making. To this end, in the present study, based on a recent heuristic algorithm proposed by the authors, the time evolution of COVID-19 is investigated for six different countries/states, namely New York, California, USA, Iran, Sweden and UK. The number of COVID-19-related deaths is used to develop the proposed heuristic model as it is believed that the predicted number of daily deaths in each country/state includes information about the quality of the health system in each area, the age distribution of population, geographical and environmental factors as well as other conditions. Based on derived predicted epidemic curves, a new 3D-epidemic surface is proposed to assess the epidemic phenomenon at any time of its evolution. This research highlights the potential of the proposed model as a tool which can assist in the risk assessment of the COVID-19. Mapping its development through 3D-epidemic surface can assist in revealing its dynamic nature as well as differences and similarities among different districts.
Modeling the evolution of COVID-19
arXiv: Populations and Evolution, 2020
We present two different approaches for modeling the spread of the COVID-19 pandemic. Both approaches are based on the population classes susceptible, exposed, infectious, quarantined, and recovered and allow for an arbitrary number of subgroups with different infection rates and different levels of testing. The first model is derived from a set of ordinary differential equations that incorporate the rates at which population transitions take place among classes. The other is a particle model, which is a specific case of crowd simulation model, in which the disease is transmitted through particle collisions and infection rates are varied by adjusting the particle velocities. The parameters of these two models are tuned using information on COVID-19 from the literature and country-specific data, including the effect of restrictions as they were imposed and lifted. We demonstrate the applicability of both models using data from Cyprus, for which we find that both models yield very sim...
2020
A dynamical epidemic model optimized using genetic algorithm and cross validation method to overcome the overfitting problem is proposed. The cross validation procedure is applied so that available data are split into a training subset used to fit the algorithm’s parameters, and a smaller subset used for validation. This process is tested on the countries of Italy, Spain, Germany and South Korea before being applied to Algeria. Interestingly, our study reveals an inverse relationship between the size of the training sample and the number of generations required in the genetic algorithm. Moreover, the enhanced compartmental model presented in this work is proven to be a reliable tool to estimate key epidemic parameters and non-measurable asymptomatic infected portion of the susceptible population in order to establish realistic nowcast and forecast of epidemic’s evolution. The model is employed to study the COVID-19 outbreak dynamics in Algeria between February 25th and May 24th, 202...
Pandemic Equation for Describing and Predicting COVID19 Evolution
Journal of Healthcare Informatics Research, 2021
The purpose of this work is to describe the dynamics of the COVID-19 pandemics accounting for the mitigation measures, for the introduction or removal of the quarantine, and for the effect of vaccination when and if introduced. The methods used include the derivation of the Pandemic Equation describing the mitigation measures via the evolution of the growth time constant in the Pandemic Equation resulting in an asymmetric pandemic curve with a steeper rise than a decrease and mitigation measures. The Pandemic Equation predicts how the quarantine removal and business opening lead to a spike in the pandemic curve. The effective vaccination reduces the new daily infections predicted by the Pandemic Equation. The pandemic curves in many localities have similar time dependencies but shifted in time. The Pandemic Equation parameters extracted from the well advanced pandemic curves can be used for predicting the pandemic evolution in the localities, where the pandemics is still in the initial stages. Using the multiple pandemic locations for the parameter extraction allows for the uncertainty quantification in predicting the pandemic evolution using the introduced Pandemic Equation. Compared with other pandemic models our approach allows for easier parameter extraction amenable to using Artificial Intelligence models.
Enhancing the prediction of COVID-19 evolution by combining models and data sources
2021
We are witnessing the dramatic consequences of the COVID-19 pandemic which, unfortunately, go beyond the impact on the health system. Until herd immunity is achieved with vaccines, the only available mechanisms for controlling the pandemic are quarantines, perimeter closures and social distancing with the aim of reducing mobility. Governments only apply these measures for a reduced period of time, since they involve the closure of economic activities such as tourism, cultural activities or nightlife. The main criterion for establishing these measures and planning socioeconomic subsidies is the evolution of infections. Early warning systems in all countries monitor the COVID-19 pandemic evolution. However, the collapse of the health system and the unpredictability of human behaviour, among others, make it difficult to predict this evolution in the short to medium term. This article evaluates different models for the early prediction of the evolution of the COVID-19 pandemic to create...